At inference time, the model can then refer back to examples it has already seen with the retriever providing e.g. a weighted combination of nearest neighbours [1]} or any other aggregation of found samples. This memorization based approach is usually used with late fusion because, at the end of the computation, the model has access to its proposed output and also to the gold outputs of similar queries. These can be then combined together using e.g. gating.
